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Aim: To understand how the integration of contextual spatial data on land cover and human infrastructure can help reduce spatial bias in sampling effort, and improve the utilization of citizen science-based species recording schemes. By comparing four different citizen science projects, we explore how the sampling design's complexity affects the role of these spatial biases. Location: Denmark, Europe. Methods: We used a point process model to estimate the effect of land cover and human infrastructure on the intensity of observations from four different citizen science species recording schemes. We then use these results to predict areas of under- and oversampling as well as relative biodiversity 'hotspots' and 'deserts', accounting for common spatial biases introduced in unstructured sampling designs. Results: We demonstrate that the explanatory power of spatial biases such as infrastructure and human population density increased as the complexity of the sampling schemes decreased. Despite a low absolute sampling effort in agricultural landscapes, these areas still appeared oversampled compared to the observed species richness. Conversely, forests and grassland appeared undersampled despite higher absolute sampling efforts. We also present a novel and effective analytical approach to address spatial biases in unstructured sampling schemes and a new way to address such biases, when more structured sampling is not an option. Main conclusions: We show that citizen science datasets, which rely on untrained amateurs, are more heavily prone to spatial biases from infrastructure and human population density. Objectives and protocols of mass-participating projects should thus be designed with this in mind. Our results suggest that, where contextual data is available, modelling the intensity of individual observation can help understand and quantify how spatial biases affect the observed biological patterns. [ABSTRACT FROM AUTHOR]

Abstract: The occurrence of pharmaceuticals in different water bodies and the findings of effects on aquatic organisms in ecotoxicity tests have raised concerns about environmental risks of pharmaceuticals in receiving waters. Due to the fact that the amount of ecotoxicological studies has increased significantly during the last decade, probabilistic approaches for risk characterization of these compounds may be feasible. This approach was evaluated by applying it to 22 human-used pharmaceuticals covering both pharmaceuticals with a high volume and high ecotoxicity, using ecotoxicological effect data from laboratory studies and comparing these to monitoring data on the effluents from sewage treatment plants in Europe and pharmaceutical sales quantities. We found that for 19 of the 22 selected pharmaceuticals the existing data were sufficient for probabilistic risk characterizations. The subsequently modeled ratios between monitored concentrations and low-effect concentrations were mostly above a factor of 100. Compared to the current paradigm for EU environmental risk assessment where a safety factor of 10 or 100 might have been used it seems that for the modeled compounds there’s a low environmental risk. However, similarly calculated ratios for five pharmaceuticals (propranolol, ibuprofen, furosemide, ofloxacin, and ciprofloxacin) were below 100, while ibuprofen and ciprofloxacin are considered to be of high concern due to lack of ecotoxicity studies. This paper shows that by applying probabilistic approaches, existing data can be used to execute a comprehensive study on probability of impacts, thereby contributing to a more comprehensive environmental risk assessment of pharmaceuticals. [Copyright &y& Elsevier]